Few-shot learning aims to fast adapt a deep model from a few examples. While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift. We thus propose the Omni-Training framework to seamlessly bridge pre-training and meta-training for data-efficient few-shot learning. Our first contribution is a tri-flow Omni-Net architecture. Besides the joint representation flow, Omni-Net introduces two parallel flows for pre-training and meta-training, responsible for improving domain transferability and task transferability respectively. Omni-Net further coordinates the parallel flows by routing their representations via the joint-flow, enabling knowledge transfer across flows. Our second contribution is the Omni-Loss, which introduces a self-distillation strategy separately on the pre-training and meta-training objectives for boosting knowledge transfer throughout different training stages. Omni-Training is a general framework to accommodate many existing algorithms. Evaluations justify that our single framework consistently and clearly outperforms the individual state-of-the-art methods on both cross-task and cross-domain settings in a variety of classification, regression and reinforcement learning problems.

Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This is the first of four courses in the Certified Artificial Intelligence Practitioner (CAIP) professional certification. This course is meant as an entry point into the world of AI/ML. You'll learn about the business problems that AI/ML can solve, as well as the specific AI/ML technologies that can solve them.

The career path of Colombian physicist Luisa Fernanda Chaparro Sierra took her from studying the Higgs Boson at CERN, to using similar machine learning techniques to gauge perceptions of crime in the Colombian capital of Bogota. Chaparro, currently a Research Professor at Tecnológico de Monterrey in Monterrey, México, says that after finishing her Phd, she had the opportunity to be part of the DataLab (Laboratorio de Datos) of the Universidad Nacional de Colombia where she used the techniques of handling large databases to help understand the problem of the perception of security in Bogota via machine learning methods. "At CERN, we handled large amounts of data and to differentiate between signal and background; we used supervised machine learning techniques, so I used similar methods and adapted others for the case of perception of security," she says, adding that DataLab was composed of mathematicians, physicists, and engineers with knowledge in programming and statistics. "We used Twitter as our data source and reviewed tweets that talked about security in the city for a year," Chaparro says, "The goal was to design a model that would allow us to quantify something as subjective as perception." The researchers were also hoping to find a relationship between it and real crimes by comparing the results with the databases provided by the National Police.

ONLINE TRAINING, with Dr. Mira ABBOUD fees 50$ or 2.000.000 LBP, via OMT 5 days - January 23, 24, 25, 26 & 27 6:00pm - 8:00pm (UCT +2) The training covers the following topics: Python basics (lists/tuples/dictionaries) Numpy Library (slicing, boolean indexing) Data Acquisition with Pandas (Series & Dataframes) Data Manipulation (filter, aggregation & grouping, Cross-tabulation) Data Visualization Introduction to Pre-processing (outliers detection, null values, features selection, dimensionality reduction, standardization) - we will cover one or two techniques of each. Build basic classification model in Python

The graph represents a network of 1,390 Twitter users whose tweets in the requested range contained "iiot bigdata", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 16 December 2022 at 12:05 UTC. The requested start date was Friday, 16 December 2022 at 01:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 8-hour, 53-minute period from Tuesday, 13 December 2022 at 16:06 UTC to Friday, 16 December 2022 at 01:00 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

Hive, the productivity platform provider, announced the public release of HiveMind that uses Artificial Intelligence (AI) to automatically create a project plan in a matter of seconds. As Artificial Intelligence models are increasingly being integrated into content and note-taking platforms, Hive is pioneering the usage of the models' capacity for continuous learning and logical decision-making based on in-depth data. Modeled on six years of successful customer projects, HiveMind automatically sets out the steps to accomplish any goal, expediting project planning and execution. It has the ability to create project tasks based on simple suggestions, set next steps from received emails and reply based on the inbound email's content. "Today, superior performance in the marketplace comes from the depth of data you possess, and the ability to apply it quickly," said John Furneaux, Hive co-founder and CEO.

All across Europe, we are increasingly using artificial intelligence (AI) systems – sometimes without even realising it. Search engines, chatbots, machine translations, video games and other applications are now part of everyday life. Artificial intelligence can be a great asset to improve education and training for learners, educators and school leaders. However, it is important to better understand its ramifications.

Close your eyes and imagine for a moment that you're in Tatooine. Yes, the room inside the vehicle is a bit cramped, but hey, you're in the Star Wars saga so you'll take what you get. You squint into the vast expanse as Luke asks you if you see anything. But before you can celebrate, Tusken Raiders attack and your crew are hopelessly overpowered. Remember, this is Luke before he knew what the "Force" was.

The Three Laws of Robotics, which state that a robot cannot harm or allow a human to come into harm, must obey orders given by human beings, and must protect its own existence, were set out by Isaac Asimov 80 years ago, long before Artificial Intelligence became a reality. Despite this, they are still able to illustrate how humans have dealt with the ethical challenges of technology by protecting the users. Ethical challenges associated with technology are not inherently about the technology itself, but rather are a social problem. Technology, therefore, and in particular, Artificial Intelligence, could be used to empower users and help us build a more ethical society. This approach, put forward in the article, 'Ethical Idealism, Technology and Practice: a Manifesto,' will help us utilise technology for the betterment of society. There has long been the fear that humans would succeed in making machines so intelligent that they would end up rebelling against their creators.

Student success models might be prone to develop weak spots, i.e., examples hard to accurately classify due to insufficient representation during model creation. This weakness is one of the main factors undermining users' trust, since model predictions could for instance lead an instructor to not intervene on a student in need. In this paper, we unveil the need of detecting and characterizing unknown unknowns in student success prediction in order to better understand when models may fail. Unknown unknowns include the students for which the model is highly confident in its predictions, but is actually wrong. Therefore, we cannot solely rely on the model's confidence when evaluating the predictions quality. We first introduce a framework for the identification and characterization of unknown unknowns. We then assess its informativeness on log data collected from flipped courses and online courses using quantitative analyses and interviews with instructors. Our results show that unknown unknowns are a critical issue in this domain and that our framework can be applied to support their detection. The source code is available at https://github.com/epfl-ml4ed/unknown-unknowns.